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bogus comments on Fairness in machine learning decisions - Less Wrong
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Comments (18)
It's not clear to me how this "fairness" criteria is supposed to work. If you simply don't include S among the predictors, then for any given x in X, the classification of x will be 'independent' of S in that a counterfactual x' with the exact same features but different S would be classified the exact same way. OTOH if you're aiming to have Y be uncorrelated with S even without controlling for X, this essentially requires adding S as a 'predictor' too; e.g. consider the Simpson paradox. But this is a weird operationalization of 'fairness'.
Except that from the x, you can often deduce S. Suppose S is race (which seems to be what people care about in this situation) while X doesn't include race but does include, eg, race of parents.
And I'm not aiming for S uncorrelated with Y (that's what the paper's authors seem to want). I'm aiming for S uncorrelated with Y, once we take into account a small number of allowable variables T (eg income).